Análise de Sinais de Eletroencefalograma para Medição de Atenção em um Ambiente Musical Imersivo Multissensorial
Resumo
A compreensão dos ciclos de atenção em atividades criativas e dinâmicas tem ganhado destaque em pesquisas de interfaces cérebrocomputador (BCI), especialmente em ambientes imersivos que combinam estímulos auditivos, visuais e motores. Em tarefas musicais, o engajamento cognitivo pode flutuar de forma espontânea, não linear e sensível à estrutura da tarefa. Este estudo explora como diferentes tipos de estímulos sensoriais influenciam o foco atencional em uma bateria virtual imersiva. Este estudo investiga os ciclos de atenção de participantes através da captura e análise de sinais cerebrais durante a execução de tarefas musicais em um ambiente imersivo com e sem feedback tátil. Vinte e cinco indivíduos realizaram quatro exercícios rítmicos em uma bateria virtual, usando baquetas físicas com efeito vibratório (efeito háptico) em ambas. Os indivíduos tiveram três condições experimentais: livre (exploração livre), não háptica (exercícios definidos e baquetas sem efeito vibratório) e háptica (exercícios definidos e as baquetas com efeito vibratório). Os níveis de atenção foram monitorados continuamente e categorizados em três faixas (baixa, média e alta), com estimativas de tempo de ativação e tempo de permanência em cada faixa. Os dados mostraram que a atenção se concentrou no início da atividade, especialmente nos dois primeiros exercícios de ritmo mais lento e maior demanda motora. Nos exercícios finais, a ausência de alguns instrumentos pode ter sido causada pelo aumento da velocidade. Apesar de variações na faixa alta durante o uso de estímulo háptico, o teste de Wilcoxon indicou que não houve diferença significativa entre as experiências. Observou-se também que alcançar picos de atenção não implica maior tempo nesse estado. Conclui-se que os estímulos vibratórios não influenciaram de forma consistente a atenção sustentada, mas ritmo e complexidade motora podem afetar o engajamento.
Palavras-chave:
EEG, Ambiente musical imersivo, Io3MT, Nível de atenção, Interface háptica
Referências
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Carlos P Amaral, Marco A Simões, Susana Mouga, João Andrade, and Miguel Castelo-Branco. 2017. A novel brain computer interface for classification of social joint attention in autism and comparison of 3 experimental setups: a feasibility study. Journal of neuroscience methods 290 (2017), 105–115.
Vijay Anant Athavale, Nasiba Sherkuziyeva, Muntadher Abed Hussein, Israa Abed Jawad, Shyamasundar Tripathy, and S Vijaya Kumar. 2025. Real-Time Student Engagement Through Brain-Computer Interface-Controlled Tools. In Concepts and Applications of Brain-Computer Interfaces. IGI Global Scientific Publishing, 355–368.
D. Batista, H. Plácido da Silva, A. Fred, C. Moreira, M. Reis, and H. A. Ferreira. 2019. Benchmarking of the BITalino biomedical toolkit against an established gold standard. Healthcare Technology Letters 6, 2 (2019), 32–36. DOI: 10.1049/htl.2018.5037
Filippo Cavallo, Erika Rovini, Cristina Dolciotti, Lorenzo Radi, Riccardo Della Ragione, Paolo Bongioanni, and Laura Fiorini. 2020. Physiological response to Vibro-Acoustic stimulation in healthy subjects: A preliminary study. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 5921–5924.
P. Comon. 1994. Independent Component Analysis, A new concept? Signal Processing 36, 3 (1994), 287–314.
Ary L. Goldberger, Luis A. N. Amaral, Leon Glass, Jeffrey M. Hausdorff, Plamen Ch. Ivanov, Roger G. Mark, Joseph E. Mietus, George B. Moody, Chung-Kang Peng, and H. Eugene Stanley. 2000. PhysioBank, PhysioToolkit e PhysioNet: Componentes de um novo recurso de pesquisa para sinais fisiológicos complexos. Circulação [Online] 101, 23 (2000), e215–e220. DOI: 10.1161/01.CIR.101.23.e215
R.C. Gonzalez and R.E.Woods. 2009. Processamento Digital De Imagens. ADDISON WESLEY BRA. [link]
R. Hassan, M. S. Hasan, J. Hasan, M. R. Jamader, D. Eisenberg, and T. Pias. 2020. Machine learning based human attention recognition from brain-EEG signals. (2020).
E. M. Imah, E. S. Dewi, and I. G. P. A. Buditjahjanto. 2021. A Comparative Analysis of Machine Learning Methods for Joint Attention Classification in Autism Spectrum Disorder Using Electroencephalography Brain Computer Interface. International Journal of Intelligent Engineering & Systems 14, 3 (2021).
Herbert Jasper. 1958. The ten-twenty electrode system of the International Federation. Electroencephalography and Clinical Neurophysiology 10 (1958), 371–375.
George H Klem. 1999. The ten-twenty electrode system of the international federation. The international federation of clinical neurophysiology. Electroencephalogr. Clin. Neurophysiol. Suppl. 52 (1999), 3–6.
Mikaela Leandertz and Esa Ala-Ruona. 2024. Multimodal vibroacoustic music therapy for functional neurological disorder: The MTFUND clinical protocol and initial impressions from multiple perspectives. Approaches: An Interdisciplinary Journal of Music Therapy (2024).
Stéphane Mallat. 2008. A Wavelet Tour of Signal Processing: The Sparse Way (3rd ed.). Academic Press.
Anthony M Norcia, L Gregory Appelbaum, Justin M Ales, Benoit R Cottereau, and Bruno Rossion. 2015. The steady-state visual evoked potential in vision research: A review. Journal of vision 15, 6 (2015), 4–4.
Marko Punkanen, Marjo Nyberg, and Tiinapriitta Savela. 2017. Vibroacoustic Therapy in the treatment of developmental trauma: Developing safety through vibrations. Music and Medicine 9, 3 (2017), 198–201.
R. Ramos, B. Valdez-Salas, R. Zlatev, M. S. Wiener, and J. M. B. Rull. 2017. The discrete wavelet transform and its application for noise removal in localized corrosion measurements. International Journal of Corrosion 2017 (2017). DOI: 10.1155/2017/7925404
Muhammad Zain Raza, Muhammad Omais, Hafiz Muhammad Ehsan Arshad, Musab Maqsood, and Ali Ahmad Nadeem. 2025. Effectiveness of brain-computer interface (BCI)-based attention training game system for symptom reduction, behavioral enhancement, and brain function modulation in children with ADHD: A systematic review and single-arm meta-analysis. NeuroRegulation 12, 1 (2025), 51–51.
Denise Rey and Markus Neuhäuser. 2011. Wilcoxon-signed-rank test. In International encyclopedia of statistical science. Springer, 1658–1659.
Carla Estefany Caetano Silva, Daniela Gorski Trevisan, and Débora Christina Muchaluat Saade. 2025. Análise de Sinais Cerebrais para Detecção de Níveis de Atenção em Jogos Digitais. In Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS). SBC, 557–568.
John Sweller. 1988. Cognitive load during problem solving: Effects on learning. Cognitive science 12, 2 (1988), 257–285.
Muhammad Usman Tariq. 2025. Revolutionizing Communication: EEG-Based Brain-Computer Interface for Speech and Mood Detection. In Rural Social Entrepreneurship Development: Network-Based Manufacturing System Model. IGI Global Scientific Publishing, 237–264.
Christopher Torrence and Gilbert P. Compo. 1998. A Practical Guide to Wavelet Analysis. Bulletin of the American Meteorological Society 79, 1 (1998), 61–78. DOI: 10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2
Luca Turchet. 2023. Musical Metaverse: vision, opportunities, and challenges. Personal and Ubiquitous Computing 27, 5 (2023), 1811–1827.
Rômulo Vieira, Débora C Muchaluat-Saade, and Pablo César. 2023. Towards an internet of multisensory, multimedia and musical things (Io3MT) environment. In 2023 4th International Symposium on the Internet of Sounds. IEEE, 1–10.
Rômulo Vieira, Shu Wei, Thomas Röggla, Débora C. Muchaluat-Saade, and Pablo César. 2024. Immersive Io3MT Environments: Design Guidelines, Use Cases and Future Directions. In 2024 IEEE 5th International Symposium on the Internet of Sounds (IS2). 1–10. DOI: 10.1109/IS262782.2024.10704141
Zdeněk Vilímek, Jiří Kantor, and Jana Kořínková. 2021. The impact of vibroacoustic therapy on subjective perception of university students–mixed design pilot study. Univers. J. Educ. Res 9 (2021), 1409–1420.
Shraddha N Zanjat, Vishwajit Barbudhe, and Bhavana S Karmore. 2025. Cognitive Enhancement Through Direct Brain-Computer Interaction. In Neural Network Technologies and Brain-Computer Interfaces: Innovations and Applications. IGI Global Scientific Publishing, 303–326.
Dalin Zhang, Lina Yao, Xiang Zhang, SenWang,Weitong Chen, Robert Boots, and Boualem Benatallah. 2018. Cascade and parallel convolutional recurrent neural networks on EEG-based intention recognition for brain computer interface. In Proceedings of the aaai conference on artificial intelligence, Vol. 32.
Igor Zyma, Sergii Tukaev, Ivan Seleznov, Ken Kiyono, Anton Popov, Mariia Chernykh, and Oleksii Shpenkov. 2019. Electroencephalograms during Mental Arithmetic Task Performance. Data 4, 1 (2019). DOI: 10.3390/data4010014
Publicado
10/11/2025
Como Citar
SILVA, Carla Estefany Caetano; VIEIRA, Rômulo; TREVISAN, Daniela Gorski; MUCHALUAT-SAADE, Débora Christina.
Análise de Sinais de Eletroencefalograma para Medição de Atenção em um Ambiente Musical Imersivo Multissensorial. In: BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA), 31. , 2025, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 38-47.
DOI: https://doi.org/10.5753/webmedia.2025.15815.
